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Deep Learning Enabled Fraud Detection in Credit Card Transactions

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Deep Learning Enabled Fraud Detection in Credit Card Transactions International Journal of Research and Scientific Innovation (IJRSI) | Volume V, Issue VII, July 2018 | ISSN 2321–2705 Deep Learning Enabled Fraud Detection in Credit Card Transactions Reshma R S# #Research Scholar, Department of Mechanical Engineering APJ Abdul Kalam Technological University, College of Engineering Trivandrum, Kerala, India Abstract-Fraudulent activities exist in all fields of financial (AUC). One with higher AUC value is considered to be a domain; credit card is not an exception. The increased fraud in better model. credit card transaction is obviously due to its wide spread popularity since the introduction of online banking and e- A. Literature Review commerce platforms. The purpose of the fraud may be to obtain goods without paying, or to obtain unauthorized funds from an [1] discussed credit card fraud detection using autoencoder account. We cannot rely on pattern based fraud detection since and restricted boltzmann machine. In this work three credit the fraudster rarely follows any pattern. As technology advances card fraud data, German , European and Australian data were the fraudulent activities will also increase. The project aims to used . The models were built and roc were plotted. It is shown introduce deep learning technique to detect fraud in credit card that the European credit card data shows greater roc curve transactions. Deep learning consists of neural networks with compared to other, both on autoencoder and restricted many hidden layers. The project uses deep learning techniques Boltzmann machine model. This is mainly due to the huge such as Autoencoder, Restricted Boltzmann Machine, number of data points available for this model. Hence for this Variational Autoencoder and Deep Belief Network to detect work European credit card data is used. The work also shows fraud in the credit card transactions. The aim is to find the best technique among them. Unsupervised deep learning method is that the tanh function work well with autoencoder based used to evaluate fraud since the model learn from data and not model so tanh based function is used as the activation function from labels. both in hidden layer, encoder and decoder section to improve the accuracy of the function. The data is divided in the ratio Keywords: Unsupervised Learning, Deep Learning, Autoencoder, 80: 20 since the amount of fraud is much lesser so this method Variational Autoencoder, Restricted Boltzmann Machine, Deep is adapted to this work too. The work shows that deep belief network learning is the most efficient method for fraud detection. H2O framework was used by the model to understand the pattern in I. INTRODUCTION the underlying dataset. H2O is an open source available for echnology is advancing day by day and this improvement analysing big data. Multi-layered, feed forward neural T in technology has significant influence on various areas network is used by the author to find patterns in credit card including financial transactions. Nowadays credit card is the fraud. [2] Discussed the comparison of available techniques most widely used transaction method, this increased for fraud detection and their demerits. The paper points out popularity of credit card is mainly due to the ease of that simple logistic regression can be using in case of linear transaction, the increasing popularity of network banking and data and it will fail if the data is non linear. In case of decision mobile banking. Since technology is a two sided coin, there is tree even small changes in the data can affect the structure of also a great improvement in the type of fraud that can occur. the tree. Choosing splitting criteria is another demerit since its Fraudster keeps on changing his fraud pattern and rarely uses selection is too complex. It cannot handle real time data too. same pattern for fraudulent activities. This shows us that From [1] the project adopts the following features that are simple fraud detection method based on labelled data cannot mentioned below. The project aims to improve the fraud work well with all type of fraud. Its high time we should think detection model mentioned in the work by adding variational about unsupervised learning method where we are not autoencoder and deep belief network to it. The tanh activation bounded to the limitations of labelled data. The paper aims to function is used in both hidden and visible layers for getting develop unsupervised deep learning models to detect fraud in better AUC value. The method of finding AUC for credit card transactions. Credit card fraud increases day by determining efficiency of the model is also adopted from the day. The way by which fraud occur rarely follows pattern. work. Above all from the three dataset analysed in the work Unsupervised deep learning method can be used to detect the project choose European credit card fraud data since the fraud in financial transactions. The paper aims to develop four work proves that this is the best data set available for fraud unsupervised deep learning methods to detect anomaly in detection with more data points. The only disadvantage of the credit cards. The data belongs to a European credit card data data is that the data is PCA transformed with unknown which consist of about 284,807 transactions out of which only parameters so the attributes that makes the transaction fraud is few are fraud. The aim is to detect the fraudulent transactions still unknown and we cannot validate the model using other and find out the area under receiver operation characteristic data since we have to mask the data with same parameter www.rsisinternational.org Page 111 International Journal of Research and Scientific Innovation (IJRSI) | Volume V, Issue VII, July 2018 | ISSN 2321–2705 which is unknown. The advantage of the dataset is that since also contains an input, hidden and output layer. The aim of the data set is already PCA transformed we don’t need to each AE model is to reproduce the input at its output with mask the data. The dataset consist of about 2 lakh transactions minimal or zero error. During each step, reconstruction error out of which only few are fraud, this is equivalent to the real is measured and a threshold for the reconstruction error is set case problems where the fraud cases are fewer than the actual so that the model learns to reproduce with minimal error. cases. An autoencoder consist of encoders, lots of hidden layer B. Data and Methodology and decoders. The input is compressed when it traverse through the hidden layer, where the code which helps in the The datasets is credit card fraud data from a European credit regeneration of input at the output layer lies. The autoencoder card company, it is obtained from kaggle. The data contains model in this project is created using tanh fuction. Let x be transactions made by credit cards holder in 2013 September. the input of the function, h the hidden layer and x’ be the Transactions that had happened in two days is shown in the output function. The model created includes 4 layers, two dataset, It is given that the data contains only 492 frauds out encoders and two decoders. The input to the input layer is the of 284,807 transactions which accounts only 0.172 % of all input dimension of the data set. The activation function is transactions. All input variables are converted to numerical represented by a(x). In the model developed, the activation value by PCA transformation due to confidentiality issues, the function used in tanh. The equation for the encoder layer is original features are not provided and Features V1, V2, ... V28 given below. are the principal components obtained after PCA transformation , the features that are not PCA transformed are i(x) (Encoder) = f(a(x)) = tanh (Wx + b) ’Time’ and ’Amount’. Time represents the difference in The input is given to the encoder model where it is seconds between the particular transaction and first multiplied by the bias function It is then compressed in the transaction. ’Amount’ represents the money transaction that hidden layer. The input is reconstructed from its reduced form had occurred, Feature ’Class’ is a variable that shows whether by using the transpose of the activation function in the hidden a transaction is fraudulent or not value 1 shows that the layer and decoder layers. By reducing the mean square error transaction is fraud else it is non fraudulent. The fig 3.8 shows the output model is optimised ie, The model contains 4 layers the distribution of data on the data set. From this we could which contains neuron numbers 29, 7,7, 29. One of the understand that the fraud data is much lesser than the non peculiarities of the model is that autoencoder wants the fraudulent data. When time is plotted against transaction it neurons in the input and output layers to be equal. The first doesn’t show any abnormality so it is proven that we can do two layers are the encoders and the last two layers are the nothing with the time and only the data matters. So we should decoders. The model is trained for 100 epochs and batch size develop a model that should learn from the data. The of 32 and save the best performing model to a file. The Model screenshot of a part of the data is shown below. Checkpoint in Keras is used for this task . The accuracy of the model is determined by measuring the AUC score of the model. ROC curves are very useful tool for understanding the performance of unbalanced data. The true positive rate Vs false positive rate over different threshold values is plotted using ROC. The line should be as closer as possible to the upper left corner which defines high accuracy.
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